A consolidated review of AI security research from February 2026 reveals a decisive pivot in the threat landscape: the primary attack surface for enterprise AI has moved from the foundational model to the autonomous agent. For IT managers and security professionals, this means defending AI now requires securing the entire agent ecosystem—its orchestration logic, connected tools, and operational permissions.
The analysis, compiled by Security Affairs, concludes that adversaries are increasingly targeting agents due to their proactive capabilities and privileged system access, rather than focusing solely on exploiting the model in isolation. This represents a fundamental paradigm shift. AI agents are designed to plan, utilize memory, and interact with external software and APIs to execute tasks, features that create new, high-value pathways for exploitation.
Consequently, the threat landscape has expanded. Known vulnerabilities like prompt injection become critically more dangerous when deployed against an agent. A successful attack can now hijack automated workflows, leverage privileged access for lateral movement within a network, or exfiltrate data through connected services. The research highlights other agent-specific vectors, including "poisoned skills" (malicious code or data within third-party tools the agent uses) and attacks that manipulate the agent's decision-making process via its integrated toolchain.
This evolution makes securing the model itself a mere baseline. Effective defense is now ecosystemic, demanding a multi-layered strategy. The research outlines four actionable pillars for security teams:
- Tool and Skill Vetting: Implement rigorous auditing and sandboxing for all third-party skills, APIs, and tools an agent can access to prevent malicious execution.
- Orchestration Hardening: Deploy strict, context-aware data access controls and robust input validation at the agent layer to govern interactions and prevent abuse.
- Behavioral Monitoring: Establish systems for continuous monitoring to detect anomalous agent actions and deviations from planned workflows, a critical mechanism for identifying compromises.
- Cross-Team Education: Train development and security teams to understand that securing the AI model is insufficient; they must now secure the agent's entire operational environment.
The findings underscore that the security perimeter must expand to protect the full orchestration chain. While the four defensive pillars are agreed upon, open questions remain for the industry, particularly concerning the best tools for real-time agent behavioral monitoring and robust governance frameworks for data access permissions. These represent frontier challenges for ongoing research and development. For IT managers, the message is clear: the battle for AI security is now firmly focused on the agent.
二〇二六年二月的AI安全研究綜合報告揭示威脅格局出現決定性轉變：企業AI的主要攻擊面已從基礎模型轉向自主代理。對IT經理及安全專業人員而言，這意味著防護AI現在需要保障整個代理生態系統的安全——包括其協調邏輯、連接的工具以及操作權限。
由《Security Affairs》彙編的分析報告總結，由於代理具備主動能力和特權系統訪問權限，對手正日益將目標鎖定代理，而非僅專注於單獨利用模型漏洞。這代表著一個根本性的範式轉移。AI代理被設計用來規劃、利用記憶，並與外部軟件及API互動以執行任務，這些功能創造了新的、高價值的利用途徑。
因此，威脅格局已然擴大。已知漏洞如提示詞注入，當用於針對代理時會變得極其危險。成功的攻擊現在可以劫持自動化工作流程、利用特權訪問在網絡內進行橫向移動，或通過連接的服務洩露數據。研究突出了其他代理特定的攻擊向量，包括「poisoned skills」（代理使用的第三方工具中的惡意代碼或數據）以及通過整合工具鏈操縱代理決策過程的攻擊。
這種演進使得僅保護模型本身成為最低要求。有效的防禦現在必須是生態系統性的，需要採取多層次的策略。研究為安全團隊概述了四個可執行的支柱：
- 工具與技能審查： 對代理可訪問的所有第三方技能、API和工具實施嚴格審計和沙箱測試，以防止惡意執行。
- 協調強化： 在代理層部署嚴格的、基於上下文的數據訪問控制和強大的輸入驗證，以管轄互動並防止濫用。
- 行為監控： 建立持續監控系統，以檢測異常代理行為及偏離預定工作流程的情況，這是識別入侵的關鍵機制。
- 跨團隊教育： 培訓開發和安全團隊，使其理解僅保護AI模型並不足夠；他們現在必須保護代理的整個操作環境。
研究結果強調，安全邊界必須擴展以保護完整的協調鏈。儘管四個防禦支柱已獲業界認同，但仍存在待解問題，尤其在即時代理行為監控的最佳工具以及數據訪問權限的穩健治理框架方面。這些代表了持續研發的前沿挑戰。對IT經理而言，訊息很明確：AI安全之戰現在已明確聚焦於代理。